# Near-Term Quantum Computing Techniques: Variational Quantum Algorithms, Error Mitigation, Circuit Compilation, Benchmarking and Classical Simulation

arXiv: https://arxiv.org/abs/2211.08737 · HTML: https://mlsanity.com/paper/2211.08737/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 272 references that are in the map.
- First posted on arXiv 16 Nov 2022.

## Builds on (most cited first, 20 of 20)

- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Variational Quantum Algorithms](https://mlsanity.com/paper/2012.09265/) (2020)
- [Supervised learning with quantum enhanced feature spaces](https://mlsanity.com/paper/1804.11326/) (2018)
- [Barren plateaus in quantum neural network training landscapes](https://mlsanity.com/paper/1803.11173/) (2018)
- [Quantum Machine Learning](https://mlsanity.com/paper/1611.09347/) (2016)
- [Predicting Many Properties of a Quantum System from Very Few Measurements](https://mlsanity.com/paper/2002.08953/) (2020)
- [Noise-Induced Barren Plateaus in Variational Quantum Algorithms](https://mlsanity.com/paper/2007.14384/) (2020)
- [Quantum Natural Gradient](https://mlsanity.com/paper/1909.02108/) (2019)
- [A quantum-inspired classical algorithm for recommendation systems](https://mlsanity.com/paper/1807.04271/) (2018)
- [Stochastic gradient descent for hybrid quantum-classical optimization](https://mlsanity.com/paper/1910.01155/) (2019)
- [Learning to Optimize Variational Quantum Circuits to Solve Combinatorial Problems](https://mlsanity.com/paper/1911.11071/) (2019)
- [Quantum versus Classical Generative Modelling in Finance](https://mlsanity.com/paper/2008.00691/) (2020)
- [Natural Evolutionary Strategies for Variational Quantum Computation](https://mlsanity.com/paper/2012.00101/) (2020)
- [Experimental Implementation of a Quantum Autoencoder via Quantum Adders](https://mlsanity.com/paper/1807.10643/) (2018)
- [Towards a Distributed Quantum Computing Ecosystem](https://mlsanity.com/paper/2002.11808/) (2020)
- [ProjectQ: An Open Source Software Framework for Quantum Computing](https://mlsanity.com/paper/1612.08091/) (2016)
- [Optimal Space-Depth Trade-Off of CNOT Circuits in Quantum Logic Synthesis](https://mlsanity.com/paper/1907.05087/) (2019)
- [Homomorphic Encryption Experiments on IBM's Cloud Quantum Computing Platform](https://mlsanity.com/paper/1612.02886/) (2016)
- [Lifetime-based Optimization for Simulating Quantum Circuits on a New Sunway Supercomputer](https://mlsanity.com/paper/2205.00393/) (2022)

## Built on by (most cited first, 13 of 13)

- [Quantum Circuit Synthesis and Compilation Optimization: Overview and Prospects](https://mlsanity.com/paper/2407.00736/) (2024)
- [Quantum Compiling with Reinforcement Learning on a Superconducting Processor](https://mlsanity.com/paper/2406.12195/) (2024)
- [Application of quantum machine learning using quantum kernel algorithms on multiclass neuron M type classification](https://mlsanity.com/paper/2502.06281/) (2025)
- [Q-fid: Quantum Circuit Fidelity Improvement with LSTM Networks](https://mlsanity.com/paper/2303.17523/) (2023)
- [Application of Quantum Pre-Processing Filter for Binary Image Classification with Small Samples](https://mlsanity.com/paper/2308.14930/) (2023)
- [Quantum Neural Networks for Power Flow Analysis](https://mlsanity.com/paper/2311.06293/) (2023)
- [A Hierarchical Fused Quantum Fuzzy Neural Network for Image Classification](https://mlsanity.com/paper/2403.09318/) (2024)
- [On Optimizing Hyperparameters for Quantum Neural Networks](https://mlsanity.com/paper/2403.18579/) (2024)
- [Quantum Neural Network applications to Protein Binding Affinity Predictions](https://mlsanity.com/paper/2508.03446/) (2025)
- [Hybrid Quantum--Classical Machine Learning Potential with Variational Quantum Circuits](https://mlsanity.com/paper/2508.04098/) (2025)
- [Sample-efficient quantum error mitigation via classical learning surrogates](https://mlsanity.com/paper/2511.07092/) (2025)
- [Machine Failure Detection Based on Projected Quantum Models](https://mlsanity.com/paper/2601.15641/) (2026)
- [Quantum Cinema: An Interactive Cinematic Exploration of Quantum Computing Hardware via Generative World Models](https://mlsanity.com/paper/2606.17102/) (2026)

## Abstract (arXiv)

Quantum computing is a game-changing technology for global academia, research centers and industries including computational science, mathematics, finance, pharmaceutical, materials science, chemistry and cryptography. Although it has seen a major boost in the last decade, we are still a long way from reaching the maturity of a full-fledged quantum computer. That said, we will be in the Noisy-Intermediate Scale Quantum (NISQ) era for a long time, working on dozens or even thousands of qubits quantum computing systems. An outstanding challenge, then, is to come up with an application that can reliably carry out a nontrivial task of interest on the near-term quantum devices with non-negligible quantum noise. To address this challenge, several near-term quantum computing techniques, including variational quantum algorithms, error mitigation, quantum circuit compilation and benchmarking protocols, have been proposed to characterize and mitigate errors, and to implement algorithms with a certain resistance to noise, so as to enhance the capabilities of near-term quantum devices and explore the boundaries of their ability to realize useful applications. Besides, the development of near-term quantum devices is inseparable from the efficient classical simulation, which plays a vital role in quantum algorithm design and verification, error-tolerant verification and other applications. This review will provide a thorough introduction of these near-term quantum computing techniques, report on their progress, and finally discuss the future prospect of these techniques, which we hope will motivate researchers to undertake additional studies in this field.